Over the years, huge resources were committed to developing rule-based technologies, ranging from stand alone inference engines and expert systems to development tools that allowed programmers to program business rules and processes into the existing system environments. These efforts included development of sophisticated workflow and routing applications, customer relationship management (CRM) and enterprise resource planning (ERP) systems for automating portions of business transactions. Moreover, many companies have developed proprietary applications based on hard-coded rules and process configurations.
Despite these automation efforts, many front and back office operations to process complex transactions in the services industry still rely greatly on human operators working with legacy systems. Manual work processes that have these same generic characteristics exist in the insurance industry (including policy underwriting and issuance and customer service), the telecommunications industry (service provisioning, contract and billing administration), the government (Social Security Benefits administration, Medicare and Medicaid eligibility and compliance), the utilities industries (service provisioning and product bundling) or the like. Even the new economy Internet companies such as amazon.com, yahoo.com, and priceline.com are building people-intensive service infrastructures to manually handle complex service transactions.
For example, to process automobile accident claims, an auto insurer relies on a large number of both the call center agents in the front office and claims management staff in the back office to take initial notice of loss details, establish a claims case file, generate data requests from various third parties, notify repairers and assessors, communicate with policyholders, wait for data inputs and outputs in both paper and electronic format and eventually hand the case over to experienced claims adjusters for resolution.
These agents must become skilled at navigating multiple, divergent legacy claims and policy administration systems. They must learn and apply complex business rules and processes outside of these systems which are frequently changing, handle infinite combinations and permutations of incident types and know their employer's many product and benefit entitlements intimately. And while managing these complex knowledge matrices, they must also remain tuned in to the needs of the policyholder who is usually distressed and wants clear affirmation and confidence in the resolution of their claim. At each step in the manual processing of these claims there is an opportunity for human error. Each error exposes the insurer to substantial losses, and the unnecessary additional administration costs that result from the rework required to resolve the error, not to mention the frustration of the policy holder.
Therefore, it can be appreciated that there is a significant need for an improved system and method for automating such manual processes in processing business transactions.